The Role of Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Disorders
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[1] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[2] Strother H. Walker,et al. Estimation of the probability of an event as a function of several independent variables. , 1967, Biometrika.
[3] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[4] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[5] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[6] J. van Dam,et al. Guidelines for training in electronic ultrasound: guidelines for clinical application. From the ASGE. American Society for Gastrointestinal Endoscopy. , 1999, Gastrointestinal endoscopy.
[7] J. Ross Quinlan,et al. Simplifying decision trees , 1987, Int. J. Hum. Comput. Stud..
[8] J. Dam,et al. Guidelines for training in endoscopic ultrasound , 1999 .
[9] J. Greenleaf,et al. Neural network analysis of EUS images to differentiate between pancreatic malignancy and pancreatitis. , 2001, Gastrointestinal endoscopy.
[10] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[11] Dale Schuurmans,et al. Augmenting Naive Bayes Classifiers with Statistical Language Models , 2004, Information Retrieval.
[12] William Stafford Noble,et al. Support vector machine , 2013 .
[13] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[14] Kunio Doi,et al. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..
[15] Baoxin Li,et al. Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. , 2008, Gastrointestinal endoscopy.
[16] Zhaoshen Li,et al. Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images. , 2010, Gastrointestinal endoscopy.
[17] P. Vilmann,et al. Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses. , 2012, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.
[18] Masao Tanaka,et al. Japan Pancreatic Cancer Registry; 30th Year Anniversary: Japan Pancreas Society , 2012, Pancreas.
[19] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[20] Can Xu,et al. Differentiation of Pancreatic Cancer and Chronic Pancreatitis Using Computer-Aided Diagnosis of Endoscopic Ultrasound (EUS) Images: A Diagnostic Test , 2013, PloS one.
[21] A. Ignee,et al. Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos). , 2015, Gastrointestinal endoscopy.
[22] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[24] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[26] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[27] O. Kocaman,et al. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images , 2016, Endoscopic ultrasound.
[28] Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breast Tissue. , 2018, AJR. American journal of roentgenology.
[29] Hsuan-Ting Chang,et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. , 2017, Gastrointestinal endoscopy.
[30] R. Turkington,et al. Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment and outcomes , 2018, World journal of gastroenterology.
[31] Bin Dong,et al. End-to-End Lung Nodule Detection in Computed Tomography , 2017, MLMI@MICCAI.
[32] Osamu Abe,et al. Deep learning with convolutional neural network in radiology , 2018, Japanese Journal of Radiology.
[33] T. Tamura,et al. Impact of endoscopic ultrasonography on diagnosis of pancreatic cancer , 2018, Journal of Gastroenterology.
[34] M. Abràmoff,et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices , 2018, npj Digital Medicine.
[35] K. Mori,et al. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy , 2018, Annals of Internal Medicine.
[36] T. Matsuda,et al. Estimation of Invasion Depth: The First Key to Successful Colorectal ESD , 2019, Clinical endoscopy.
[37] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[38] Takamichi Kuwahara,et al. Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas , 2019, Clinical and translational gastroenterology.
[39] H. Goyal,et al. Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer , 2020, Journal of clinical medicine.
[40] Jun Zhang,et al. Deep-learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). , 2020, Gastrointestinal endoscopy.
[41] Y. Nagakawa,et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study , 2020, Journal of hepato-biliary-pancreatic sciences.